Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations10127
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory184.0 B

Variable types

Numeric17
Categorical6

Alerts

Attrition_Flag is highly overall correlated with Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1 and 1 other fieldsHigh correlation
Avg_Open_To_Buy is highly overall correlated with Avg_Utilization_Ratio and 1 other fieldsHigh correlation
Avg_Utilization_Ratio is highly overall correlated with Avg_Open_To_Buy and 1 other fieldsHigh correlation
Contacts_Count_12_mon is highly overall correlated with Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1 and 1 other fieldsHigh correlation
Credit_Limit is highly overall correlated with Avg_Open_To_BuyHigh correlation
Customer_Age is highly overall correlated with Months_on_bookHigh correlation
Gender is highly overall correlated with Income_CategoryHigh correlation
Income_Category is highly overall correlated with GenderHigh correlation
Months_Inactive_12_mon is highly overall correlated with Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1 and 1 other fieldsHigh correlation
Months_on_book is highly overall correlated with Customer_AgeHigh correlation
Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1 is highly overall correlated with Attrition_Flag and 3 other fieldsHigh correlation
Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2 is highly overall correlated with Attrition_Flag and 3 other fieldsHigh correlation
Total_Revolving_Bal is highly overall correlated with Avg_Utilization_RatioHigh correlation
Total_Trans_Amt is highly overall correlated with Total_Trans_CtHigh correlation
Total_Trans_Ct is highly overall correlated with Total_Trans_AmtHigh correlation
Card_Category is highly imbalanced (79.2%) Imbalance
CLIENTNUM has unique values Unique
Dependent_count has 904 (8.9%) zeros Zeros
Contacts_Count_12_mon has 399 (3.9%) zeros Zeros
Total_Revolving_Bal has 2470 (24.4%) zeros Zeros
Avg_Utilization_Ratio has 2470 (24.4%) zeros Zeros

Reproduction

Analysis started2025-09-10 15:15:43.083341
Analysis finished2025-09-10 15:16:03.414961
Duration20.33 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CLIENTNUM
Real number (ℝ)

Unique 

Distinct10127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3917761 × 108
Minimum7.0808208 × 108
Maximum8.2834308 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-09-10T16:16:03.521881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum7.0808208 × 108
5-th percentile7.0912039 × 108
Q17.1303677 × 108
median7.1792636 × 108
Q37.7314353 × 108
95-th percentile8.1421203 × 108
Maximum8.2834308 × 108
Range1.20261 × 108
Interquartile range (IQR)60106762

Descriptive statistics

Standard deviation36903783
Coefficient of variation (CV)0.049925462
Kurtosis-0.6156397
Mean7.3917761 × 108
Median Absolute Deviation (MAD)6347700
Skewness0.99560101
Sum7.4856516 × 1012
Variance1.3618892 × 1015
MonotonicityNot monotonic
2025-09-10T16:16:03.604745image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
768805383 1
 
< 0.1%
711784908 1
 
< 0.1%
720133908 1
 
< 0.1%
803197833 1
 
< 0.1%
812222208 1
 
< 0.1%
757634583 1
 
< 0.1%
719362458 1
 
< 0.1%
789331908 1
 
< 0.1%
715616358 1
 
< 0.1%
806900508 1
 
< 0.1%
Other values (10117) 10117
99.9%
ValueCountFrequency (%)
708082083 1
< 0.1%
708083283 1
< 0.1%
708084558 1
< 0.1%
708085458 1
< 0.1%
708086958 1
< 0.1%
708095133 1
< 0.1%
708098133 1
< 0.1%
708099183 1
< 0.1%
708100533 1
< 0.1%
708103608 1
< 0.1%
ValueCountFrequency (%)
828343083 1
< 0.1%
828298908 1
< 0.1%
828294933 1
< 0.1%
828291858 1
< 0.1%
828288333 1
< 0.1%
828285858 1
< 0.1%
828281733 1
< 0.1%
828236133 1
< 0.1%
828227433 1
< 0.1%
828215508 1
< 0.1%

Attrition_Flag
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Existing Customer
8500 
Attrited Customer
1627 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters172159
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExisting Customer
2nd rowExisting Customer
3rd rowExisting Customer
4th rowExisting Customer
5th rowExisting Customer

Common Values

ValueCountFrequency (%)
Existing Customer 8500
83.9%
Attrited Customer 1627
 
16.1%

Length

2025-09-10T16:16:03.687579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-10T16:16:03.746208image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
customer 10127
50.0%
existing 8500
42.0%
attrited 1627
 
8.0%

Most occurring characters

ValueCountFrequency (%)
t 23508
13.7%
i 18627
10.8%
s 18627
10.8%
e 11754
 
6.8%
r 11754
 
6.8%
10127
 
5.9%
C 10127
 
5.9%
u 10127
 
5.9%
o 10127
 
5.9%
m 10127
 
5.9%
Other values (6) 37254
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 172159
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 23508
13.7%
i 18627
10.8%
s 18627
10.8%
e 11754
 
6.8%
r 11754
 
6.8%
10127
 
5.9%
C 10127
 
5.9%
u 10127
 
5.9%
o 10127
 
5.9%
m 10127
 
5.9%
Other values (6) 37254
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 172159
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 23508
13.7%
i 18627
10.8%
s 18627
10.8%
e 11754
 
6.8%
r 11754
 
6.8%
10127
 
5.9%
C 10127
 
5.9%
u 10127
 
5.9%
o 10127
 
5.9%
m 10127
 
5.9%
Other values (6) 37254
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 172159
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 23508
13.7%
i 18627
10.8%
s 18627
10.8%
e 11754
 
6.8%
r 11754
 
6.8%
10127
 
5.9%
C 10127
 
5.9%
u 10127
 
5.9%
o 10127
 
5.9%
m 10127
 
5.9%
Other values (6) 37254
21.6%

Customer_Age
Real number (ℝ)

High correlation 

Distinct45
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.32596
Minimum26
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-09-10T16:16:03.817594image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile33
Q141
median46
Q352
95-th percentile60
Maximum73
Range47
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.016814
Coefficient of variation (CV)0.1730523
Kurtosis-0.28861992
Mean46.32596
Median Absolute Deviation (MAD)6
Skewness-0.033605016
Sum469143
Variance64.269307
MonotonicityNot monotonic
2025-09-10T16:16:03.901207image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
44 500
 
4.9%
49 495
 
4.9%
46 490
 
4.8%
45 486
 
4.8%
47 479
 
4.7%
43 473
 
4.7%
48 472
 
4.7%
50 452
 
4.5%
42 426
 
4.2%
51 398
 
3.9%
Other values (35) 5456
53.9%
ValueCountFrequency (%)
26 78
0.8%
27 32
 
0.3%
28 29
 
0.3%
29 56
 
0.6%
30 70
 
0.7%
31 91
0.9%
32 106
1.0%
33 127
1.3%
34 146
1.4%
35 184
1.8%
ValueCountFrequency (%)
73 1
 
< 0.1%
70 1
 
< 0.1%
68 2
 
< 0.1%
67 4
 
< 0.1%
66 2
 
< 0.1%
65 101
1.0%
64 43
0.4%
63 65
0.6%
62 93
0.9%
61 93
0.9%

Gender
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
F
5358 
M
4769 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Length

2025-09-10T16:16:03.972220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-10T16:16:04.023967image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
f 5358
52.9%
m 4769
47.1%

Most occurring characters

ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Dependent_count
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3462032
Minimum0
Maximum5
Zeros904
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-09-10T16:16:04.069643image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2989083
Coefficient of variation (CV)0.55362142
Kurtosis-0.68301665
Mean2.3462032
Median Absolute Deviation (MAD)1
Skewness-0.020825536
Sum23760
Variance1.6871629
MonotonicityNot monotonic
2025-09-10T16:16:04.142153image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2732
27.0%
2 2655
26.2%
1 1838
18.1%
4 1574
15.5%
0 904
 
8.9%
5 424
 
4.2%
ValueCountFrequency (%)
0 904
 
8.9%
1 1838
18.1%
2 2655
26.2%
3 2732
27.0%
4 1574
15.5%
5 424
 
4.2%
ValueCountFrequency (%)
5 424
 
4.2%
4 1574
15.5%
3 2732
27.0%
2 2655
26.2%
1 1838
18.1%
0 904
 
8.9%

Education_Level
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Graduate
3128 
High School
2013 
Unknown
1519 
Uneducated
1487 
College
1013 
Other values (2)
967 

Length

Max length13
Median length11
Mean length8.9392713
Min length7

Characters and Unicode

Total characters90528
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh School
2nd rowGraduate
3rd rowGraduate
4th rowHigh School
5th rowUneducated

Common Values

ValueCountFrequency (%)
Graduate 3128
30.9%
High School 2013
19.9%
Unknown 1519
15.0%
Uneducated 1487
14.7%
College 1013
 
10.0%
Post-Graduate 516
 
5.1%
Doctorate 451
 
4.5%

Length

2025-09-10T16:16:04.255382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-10T16:16:04.341949image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
graduate 3128
25.8%
high 2013
16.6%
school 2013
16.6%
unknown 1519
12.5%
uneducated 1487
12.2%
college 1013
 
8.3%
post-graduate 516
 
4.3%
doctorate 451
 
3.7%

Most occurring characters

ValueCountFrequency (%)
a 9226
 
10.2%
e 9095
 
10.0%
o 7976
 
8.8%
d 6618
 
7.3%
t 6549
 
7.2%
n 6044
 
6.7%
u 5131
 
5.7%
r 4095
 
4.5%
l 4039
 
4.5%
h 4026
 
4.4%
Other values (15) 27729
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 9226
 
10.2%
e 9095
 
10.0%
o 7976
 
8.8%
d 6618
 
7.3%
t 6549
 
7.2%
n 6044
 
6.7%
u 5131
 
5.7%
r 4095
 
4.5%
l 4039
 
4.5%
h 4026
 
4.4%
Other values (15) 27729
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 9226
 
10.2%
e 9095
 
10.0%
o 7976
 
8.8%
d 6618
 
7.3%
t 6549
 
7.2%
n 6044
 
6.7%
u 5131
 
5.7%
r 4095
 
4.5%
l 4039
 
4.5%
h 4026
 
4.4%
Other values (15) 27729
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 9226
 
10.2%
e 9095
 
10.0%
o 7976
 
8.8%
d 6618
 
7.3%
t 6549
 
7.2%
n 6044
 
6.7%
u 5131
 
5.7%
r 4095
 
4.5%
l 4039
 
4.5%
h 4026
 
4.4%
Other values (15) 27729
30.6%

Marital_Status
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Married
4687 
Single
3943 
Unknown
749 
Divorced
748 

Length

Max length8
Median length7
Mean length6.6845068
Min length6

Characters and Unicode

Total characters67694
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowSingle
3rd rowMarried
4th rowUnknown
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 4687
46.3%
Single 3943
38.9%
Unknown 749
 
7.4%
Divorced 748
 
7.4%

Length

2025-09-10T16:16:04.422824image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-10T16:16:04.484575image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
married 4687
46.3%
single 3943
38.9%
unknown 749
 
7.4%
divorced 748
 
7.4%

Most occurring characters

ValueCountFrequency (%)
r 10122
15.0%
i 9378
13.9%
e 9378
13.9%
n 6190
9.1%
d 5435
8.0%
M 4687
6.9%
a 4687
6.9%
l 3943
 
5.8%
g 3943
 
5.8%
S 3943
 
5.8%
Other values (7) 5988
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 10122
15.0%
i 9378
13.9%
e 9378
13.9%
n 6190
9.1%
d 5435
8.0%
M 4687
6.9%
a 4687
6.9%
l 3943
 
5.8%
g 3943
 
5.8%
S 3943
 
5.8%
Other values (7) 5988
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 10122
15.0%
i 9378
13.9%
e 9378
13.9%
n 6190
9.1%
d 5435
8.0%
M 4687
6.9%
a 4687
6.9%
l 3943
 
5.8%
g 3943
 
5.8%
S 3943
 
5.8%
Other values (7) 5988
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 10122
15.0%
i 9378
13.9%
e 9378
13.9%
n 6190
9.1%
d 5435
8.0%
M 4687
6.9%
a 4687
6.9%
l 3943
 
5.8%
g 3943
 
5.8%
S 3943
 
5.8%
Other values (7) 5988
8.8%

Income_Category
Categorical

High correlation 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Less than $40K
3561 
$40K - $60K
1790 
$80K - $120K
1535 
$60K - $80K
1402 
Unknown
1112 

Length

Max length14
Median length12
Mean length11.480103
Min length7

Characters and Unicode

Total characters116259
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$60K - $80K
2nd rowLess than $40K
3rd row$80K - $120K
4th rowLess than $40K
5th row$60K - $80K

Common Values

ValueCountFrequency (%)
Less than $40K 3561
35.2%
$40K - $60K 1790
17.7%
$80K - $120K 1535
15.2%
$60K - $80K 1402
 
13.8%
Unknown 1112
 
11.0%
$120K + 727
 
7.2%

Length

2025-09-10T16:16:04.567905image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-10T16:16:04.650631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
5454
19.9%
40k 5351
19.5%
less 3561
13.0%
than 3561
13.0%
60k 3192
11.6%
80k 2937
10.7%
120k 2262
8.2%
unknown 1112
 
4.1%

Most occurring characters

ValueCountFrequency (%)
17303
14.9%
K 13742
11.8%
0 13742
11.8%
$ 13742
11.8%
s 7122
 
6.1%
n 6897
 
5.9%
4 5351
 
4.6%
- 4727
 
4.1%
e 3561
 
3.1%
L 3561
 
3.1%
Other values (12) 26511
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 116259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17303
14.9%
K 13742
11.8%
0 13742
11.8%
$ 13742
11.8%
s 7122
 
6.1%
n 6897
 
5.9%
4 5351
 
4.6%
- 4727
 
4.1%
e 3561
 
3.1%
L 3561
 
3.1%
Other values (12) 26511
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 116259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17303
14.9%
K 13742
11.8%
0 13742
11.8%
$ 13742
11.8%
s 7122
 
6.1%
n 6897
 
5.9%
4 5351
 
4.6%
- 4727
 
4.1%
e 3561
 
3.1%
L 3561
 
3.1%
Other values (12) 26511
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 116259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17303
14.9%
K 13742
11.8%
0 13742
11.8%
$ 13742
11.8%
s 7122
 
6.1%
n 6897
 
5.9%
4 5351
 
4.6%
- 4727
 
4.1%
e 3561
 
3.1%
L 3561
 
3.1%
Other values (12) 26511
22.8%

Card_Category
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Blue
9436 
Silver
 
555
Gold
 
116
Platinum
 
20

Length

Max length8
Median length4
Mean length4.1175077
Min length4

Characters and Unicode

Total characters41698
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue
2nd rowBlue
3rd rowBlue
4th rowBlue
5th rowBlue

Common Values

ValueCountFrequency (%)
Blue 9436
93.2%
Silver 555
 
5.5%
Gold 116
 
1.1%
Platinum 20
 
0.2%

Length

2025-09-10T16:16:05.008729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-10T16:16:05.075472image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
blue 9436
93.2%
silver 555
 
5.5%
gold 116
 
1.1%
platinum 20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
B 9436
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
B 9436
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
B 9436
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
B 9436
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Months_on_book
Real number (ℝ)

High correlation 

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.928409
Minimum13
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-09-10T16:16:05.135953image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile22
Q131
median36
Q340
95-th percentile50
Maximum56
Range43
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.9864163
Coefficient of variation (CV)0.22228695
Kurtosis0.40010012
Mean35.928409
Median Absolute Deviation (MAD)4
Skewness-0.10656536
Sum363847
Variance63.782846
MonotonicityNot monotonic
2025-09-10T16:16:05.208705image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
36 2463
24.3%
37 358
 
3.5%
34 353
 
3.5%
38 347
 
3.4%
39 341
 
3.4%
40 333
 
3.3%
31 318
 
3.1%
35 317
 
3.1%
33 305
 
3.0%
30 300
 
3.0%
Other values (34) 4692
46.3%
ValueCountFrequency (%)
13 70
0.7%
14 16
 
0.2%
15 34
 
0.3%
16 29
 
0.3%
17 39
 
0.4%
18 58
0.6%
19 63
0.6%
20 74
0.7%
21 83
0.8%
22 105
1.0%
ValueCountFrequency (%)
56 103
1.0%
55 42
 
0.4%
54 53
 
0.5%
53 78
0.8%
52 62
 
0.6%
51 80
0.8%
50 96
0.9%
49 141
1.4%
48 162
1.6%
47 171
1.7%

Total_Relationship_Count
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8125802
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-09-10T16:16:05.275644image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5544079
Coefficient of variation (CV)0.40770496
Kurtosis-1.0061305
Mean3.8125802
Median Absolute Deviation (MAD)1
Skewness-0.16245241
Sum38610
Variance2.4161838
MonotonicityNot monotonic
2025-09-10T16:16:05.329976image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2305
22.8%
4 1912
18.9%
5 1891
18.7%
6 1866
18.4%
2 1243
12.3%
1 910
 
9.0%
ValueCountFrequency (%)
1 910
 
9.0%
2 1243
12.3%
3 2305
22.8%
4 1912
18.9%
5 1891
18.7%
6 1866
18.4%
ValueCountFrequency (%)
6 1866
18.4%
5 1891
18.7%
4 1912
18.9%
3 2305
22.8%
2 1243
12.3%
1 910
 
9.0%

Months_Inactive_12_mon
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3411672
Minimum0
Maximum6
Zeros29
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-09-10T16:16:05.384928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0106224
Coefficient of variation (CV)0.4316746
Kurtosis1.0985226
Mean2.3411672
Median Absolute Deviation (MAD)1
Skewness0.63306113
Sum23709
Variance1.0213576
MonotonicityNot monotonic
2025-09-10T16:16:05.439027image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3846
38.0%
2 3282
32.4%
1 2233
22.0%
4 435
 
4.3%
5 178
 
1.8%
6 124
 
1.2%
0 29
 
0.3%
ValueCountFrequency (%)
0 29
 
0.3%
1 2233
22.0%
2 3282
32.4%
3 3846
38.0%
4 435
 
4.3%
5 178
 
1.8%
6 124
 
1.2%
ValueCountFrequency (%)
6 124
 
1.2%
5 178
 
1.8%
4 435
 
4.3%
3 3846
38.0%
2 3282
32.4%
1 2233
22.0%
0 29
 
0.3%

Contacts_Count_12_mon
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4553175
Minimum0
Maximum6
Zeros399
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-09-10T16:16:05.502309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1062251
Coefficient of variation (CV)0.45054261
Kurtosis0.00086265663
Mean2.4553175
Median Absolute Deviation (MAD)1
Skewness0.011005626
Sum24865
Variance1.2237341
MonotonicityNot monotonic
2025-09-10T16:16:05.572687image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3380
33.4%
2 3227
31.9%
1 1499
14.8%
4 1392
13.7%
0 399
 
3.9%
5 176
 
1.7%
6 54
 
0.5%
ValueCountFrequency (%)
0 399
 
3.9%
1 1499
14.8%
2 3227
31.9%
3 3380
33.4%
4 1392
13.7%
5 176
 
1.7%
6 54
 
0.5%
ValueCountFrequency (%)
6 54
 
0.5%
5 176
 
1.7%
4 1392
13.7%
3 3380
33.4%
2 3227
31.9%
1 1499
14.8%
0 399
 
3.9%

Credit_Limit
Real number (ℝ)

High correlation 

Distinct6205
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8631.9537
Minimum1438.3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-09-10T16:16:05.651256image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1438.3
5-th percentile1438.51
Q12555
median4549
Q311067.5
95-th percentile34516
Maximum34516
Range33077.7
Interquartile range (IQR)8512.5

Descriptive statistics

Standard deviation9088.7767
Coefficient of variation (CV)1.0529223
Kurtosis1.8089893
Mean8631.9537
Median Absolute Deviation (MAD)2593
Skewness1.6667258
Sum87415795
Variance82605861
MonotonicityNot monotonic
2025-09-10T16:16:05.745048image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34516 508
 
5.0%
1438.3 507
 
5.0%
9959 18
 
0.2%
15987 18
 
0.2%
23981 12
 
0.1%
2490 11
 
0.1%
6224 11
 
0.1%
3735 11
 
0.1%
7469 10
 
0.1%
2069 8
 
0.1%
Other values (6195) 9013
89.0%
ValueCountFrequency (%)
1438.3 507
5.0%
1439 2
 
< 0.1%
1440 1
 
< 0.1%
1441 2
 
< 0.1%
1442 1
 
< 0.1%
1443 3
 
< 0.1%
1446 1
 
< 0.1%
1449 2
 
< 0.1%
1451 2
 
< 0.1%
1452 2
 
< 0.1%
ValueCountFrequency (%)
34516 508
5.0%
34496 1
 
< 0.1%
34458 1
 
< 0.1%
34427 1
 
< 0.1%
34198 1
 
< 0.1%
34173 1
 
< 0.1%
34162 1
 
< 0.1%
34140 1
 
< 0.1%
34058 1
 
< 0.1%
34010 1
 
< 0.1%

Total_Revolving_Bal
Real number (ℝ)

High correlation  Zeros 

Distinct1974
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.8141
Minimum0
Maximum2517
Zeros2470
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-09-10T16:16:05.832913image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1359
median1276
Q31784
95-th percentile2517
Maximum2517
Range2517
Interquartile range (IQR)1425

Descriptive statistics

Standard deviation814.98734
Coefficient of variation (CV)0.70087503
Kurtosis-1.1459918
Mean1162.8141
Median Absolute Deviation (MAD)591
Skewness-0.14883725
Sum11775818
Variance664204.36
MonotonicityNot monotonic
2025-09-10T16:16:05.906892image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2470
 
24.4%
2517 508
 
5.0%
1965 12
 
0.1%
1480 12
 
0.1%
1434 11
 
0.1%
1664 11
 
0.1%
1720 11
 
0.1%
1590 10
 
0.1%
1542 10
 
0.1%
1528 10
 
0.1%
Other values (1964) 7062
69.7%
ValueCountFrequency (%)
0 2470
24.4%
132 1
 
< 0.1%
134 1
 
< 0.1%
145 1
 
< 0.1%
154 1
 
< 0.1%
157 1
 
< 0.1%
159 2
 
< 0.1%
168 2
 
< 0.1%
170 1
 
< 0.1%
186 1
 
< 0.1%
ValueCountFrequency (%)
2517 508
5.0%
2514 3
 
< 0.1%
2513 1
 
< 0.1%
2512 2
 
< 0.1%
2511 1
 
< 0.1%
2509 2
 
< 0.1%
2508 2
 
< 0.1%
2507 4
 
< 0.1%
2506 1
 
< 0.1%
2505 3
 
< 0.1%

Avg_Open_To_Buy
Real number (ℝ)

High correlation 

Distinct6813
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7469.1396
Minimum3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-09-10T16:16:05.990983image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile480.3
Q11324.5
median3474
Q39859
95-th percentile32183.4
Maximum34516
Range34513
Interquartile range (IQR)8534.5

Descriptive statistics

Standard deviation9090.6853
Coefficient of variation (CV)1.2170994
Kurtosis1.7986173
Mean7469.1396
Median Absolute Deviation (MAD)2665
Skewness1.6616965
Sum75639977
Variance82640560
MonotonicityNot monotonic
2025-09-10T16:16:06.075459image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.3 324
 
3.2%
34516 98
 
1.0%
31999 26
 
0.3%
787 8
 
0.1%
701 7
 
0.1%
713 7
 
0.1%
953 7
 
0.1%
463 7
 
0.1%
990 6
 
0.1%
788 6
 
0.1%
Other values (6803) 9631
95.1%
ValueCountFrequency (%)
3 1
< 0.1%
10 1
< 0.1%
14 2
< 0.1%
15 1
< 0.1%
24 1
< 0.1%
28 1
< 0.1%
29 1
< 0.1%
36 1
< 0.1%
39 2
< 0.1%
41 2
< 0.1%
ValueCountFrequency (%)
34516 98
1.0%
34362 1
 
< 0.1%
34302 1
 
< 0.1%
34300 1
 
< 0.1%
34297 1
 
< 0.1%
34286 1
 
< 0.1%
34238 1
 
< 0.1%
34227 1
 
< 0.1%
34140 1
 
< 0.1%
34119 1
 
< 0.1%

Total_Amt_Chng_Q4_Q1
Real number (ℝ)

Distinct1158
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75994065
Minimum0
Maximum3.397
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-09-10T16:16:06.148044image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.463
Q10.631
median0.736
Q30.859
95-th percentile1.103
Maximum3.397
Range3.397
Interquartile range (IQR)0.228

Descriptive statistics

Standard deviation0.21920677
Coefficient of variation (CV)0.28845248
Kurtosis9.9935012
Mean0.75994065
Median Absolute Deviation (MAD)0.114
Skewness1.7320634
Sum7695.919
Variance0.048051608
MonotonicityNot monotonic
2025-09-10T16:16:06.226954image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.791 36
 
0.4%
0.712 34
 
0.3%
0.743 34
 
0.3%
0.718 33
 
0.3%
0.735 33
 
0.3%
0.744 32
 
0.3%
0.699 32
 
0.3%
0.722 32
 
0.3%
0.731 31
 
0.3%
0.631 31
 
0.3%
Other values (1148) 9799
96.8%
ValueCountFrequency (%)
0 5
< 0.1%
0.01 1
 
< 0.1%
0.018 1
 
< 0.1%
0.046 1
 
< 0.1%
0.061 2
 
< 0.1%
0.072 1
 
< 0.1%
0.101 1
 
< 0.1%
0.12 1
 
< 0.1%
0.153 1
 
< 0.1%
0.163 1
 
< 0.1%
ValueCountFrequency (%)
3.397 1
< 0.1%
3.355 1
< 0.1%
2.675 1
< 0.1%
2.594 1
< 0.1%
2.368 1
< 0.1%
2.357 1
< 0.1%
2.316 1
< 0.1%
2.282 1
< 0.1%
2.275 1
< 0.1%
2.271 1
< 0.1%

Total_Trans_Amt
Real number (ℝ)

High correlation 

Distinct5033
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4404.0863
Minimum510
Maximum18484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-09-10T16:16:06.320951image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum510
5-th percentile1283.3
Q12155.5
median3899
Q34741
95-th percentile14212
Maximum18484
Range17974
Interquartile range (IQR)2585.5

Descriptive statistics

Standard deviation3397.1293
Coefficient of variation (CV)0.77135847
Kurtosis3.8940234
Mean4404.0863
Median Absolute Deviation (MAD)1308
Skewness2.0410034
Sum44600182
Variance11540487
MonotonicityNot monotonic
2025-09-10T16:16:06.416242image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4253 11
 
0.1%
4509 11
 
0.1%
4518 10
 
0.1%
2229 10
 
0.1%
4220 9
 
0.1%
4869 9
 
0.1%
4037 9
 
0.1%
4313 9
 
0.1%
4498 9
 
0.1%
4042 9
 
0.1%
Other values (5023) 10031
99.1%
ValueCountFrequency (%)
510 1
< 0.1%
530 1
< 0.1%
563 1
< 0.1%
569 1
< 0.1%
594 1
< 0.1%
596 1
< 0.1%
597 1
< 0.1%
602 1
< 0.1%
615 1
< 0.1%
643 1
< 0.1%
ValueCountFrequency (%)
18484 1
< 0.1%
17995 1
< 0.1%
17744 1
< 0.1%
17634 1
< 0.1%
17628 1
< 0.1%
17498 1
< 0.1%
17437 1
< 0.1%
17390 1
< 0.1%
17350 1
< 0.1%
17258 1
< 0.1%

Total_Trans_Ct
Real number (ℝ)

High correlation 

Distinct126
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.858695
Minimum10
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-09-10T16:16:06.487796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile28
Q145
median67
Q381
95-th percentile105
Maximum139
Range129
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.47257
Coefficient of variation (CV)0.36190322
Kurtosis-0.36716324
Mean64.858695
Median Absolute Deviation (MAD)17
Skewness0.15367307
Sum656824
Variance550.96156
MonotonicityNot monotonic
2025-09-10T16:16:06.574513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 208
 
2.1%
71 203
 
2.0%
75 203
 
2.0%
69 202
 
2.0%
82 202
 
2.0%
76 198
 
2.0%
77 197
 
1.9%
70 193
 
1.9%
74 190
 
1.9%
78 190
 
1.9%
Other values (116) 8141
80.4%
ValueCountFrequency (%)
10 4
 
< 0.1%
11 2
 
< 0.1%
12 4
 
< 0.1%
13 5
 
< 0.1%
14 9
 
0.1%
15 16
0.2%
16 13
0.1%
17 13
0.1%
18 23
0.2%
19 11
0.1%
ValueCountFrequency (%)
139 1
 
< 0.1%
138 1
 
< 0.1%
134 1
 
< 0.1%
132 1
 
< 0.1%
131 6
0.1%
130 5
< 0.1%
129 6
0.1%
128 10
0.1%
127 12
0.1%
126 10
0.1%

Total_Ct_Chng_Q4_Q1
Real number (ℝ)

Distinct830
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71222238
Minimum0
Maximum3.714
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-09-10T16:16:06.655978image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.368
Q10.582
median0.702
Q30.818
95-th percentile1.069
Maximum3.714
Range3.714
Interquartile range (IQR)0.236

Descriptive statistics

Standard deviation0.23808609
Coefficient of variation (CV)0.33428617
Kurtosis15.689293
Mean0.71222238
Median Absolute Deviation (MAD)0.119
Skewness2.0640306
Sum7212.676
Variance0.056684987
MonotonicityNot monotonic
2025-09-10T16:16:06.748003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.667 171
 
1.7%
1 166
 
1.6%
0.5 161
 
1.6%
0.75 156
 
1.5%
0.6 113
 
1.1%
0.8 101
 
1.0%
0.714 92
 
0.9%
0.833 85
 
0.8%
0.778 69
 
0.7%
0.625 63
 
0.6%
Other values (820) 8950
88.4%
ValueCountFrequency (%)
0 7
0.1%
0.028 1
 
< 0.1%
0.029 1
 
< 0.1%
0.038 1
 
< 0.1%
0.053 1
 
< 0.1%
0.059 2
 
< 0.1%
0.062 1
 
< 0.1%
0.074 1
 
< 0.1%
0.077 3
< 0.1%
0.091 3
< 0.1%
ValueCountFrequency (%)
3.714 1
 
< 0.1%
3.571 1
 
< 0.1%
3.5 1
 
< 0.1%
3.25 1
 
< 0.1%
3 2
< 0.1%
2.875 1
 
< 0.1%
2.75 1
 
< 0.1%
2.571 1
 
< 0.1%
2.5 3
< 0.1%
2.429 1
 
< 0.1%

Avg_Utilization_Ratio
Real number (ℝ)

High correlation  Zeros 

Distinct964
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27489355
Minimum0
Maximum0.999
Zeros2470
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-09-10T16:16:06.820610image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.023
median0.176
Q30.503
95-th percentile0.793
Maximum0.999
Range0.999
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.27569147
Coefficient of variation (CV)1.0029026
Kurtosis-0.79497195
Mean0.27489355
Median Absolute Deviation (MAD)0.176
Skewness0.718008
Sum2783.847
Variance0.076005786
MonotonicityNot monotonic
2025-09-10T16:16:06.900092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2470
 
24.4%
0.073 44
 
0.4%
0.057 33
 
0.3%
0.048 32
 
0.3%
0.06 30
 
0.3%
0.061 29
 
0.3%
0.045 29
 
0.3%
0.059 28
 
0.3%
0.069 28
 
0.3%
0.053 27
 
0.3%
Other values (954) 7377
72.8%
ValueCountFrequency (%)
0 2470
24.4%
0.004 1
 
< 0.1%
0.005 1
 
< 0.1%
0.006 3
 
< 0.1%
0.007 1
 
< 0.1%
0.008 2
 
< 0.1%
0.009 1
 
< 0.1%
0.01 1
 
< 0.1%
0.011 1
 
< 0.1%
0.012 4
 
< 0.1%
ValueCountFrequency (%)
0.999 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.992 1
 
< 0.1%
0.99 1
 
< 0.1%
0.988 1
 
< 0.1%
0.987 1
 
< 0.1%
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 4
< 0.1%
Distinct1704
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15999746
Minimum7.6642 × 10-6
Maximum0.99958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-09-10T16:16:06.986124image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum7.6642 × 10-6
5-th percentile4.2358 × 10-5
Q19.8983 × 10-5
median0.00018146
Q30.0003373
95-th percentile0.99697
Maximum0.99958
Range0.99957234
Interquartile range (IQR)0.000238317

Descriptive statistics

Standard deviation0.36530101
Coefficient of variation (CV)2.2831675
Kurtosis1.4175352
Mean0.15999746
Median Absolute Deviation (MAD)0.000110234
Skewness1.8485384
Sum1620.2943
Variance0.13344483
MonotonicityNot monotonic
2025-09-10T16:16:07.068496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00019864 80
 
0.8%
0.0003139 78
 
0.8%
0.00030251 77
 
0.8%
0.00018665 73
 
0.7%
0.00011382 71
 
0.7%
0.00019143 66
 
0.7%
0.00011811 66
 
0.7%
0.00017987 63
 
0.6%
0.00018145 60
 
0.6%
0.00016883 59
 
0.6%
Other values (1694) 9434
93.2%
ValueCountFrequency (%)
7.6642 × 10-61
< 0.1%
7.7559 × 10-61
< 0.1%
1.0252 × 10-51
< 0.1%
1.0546 × 10-51
< 0.1%
1.1536 × 10-51
< 0.1%
1.4461 × 10-51
< 0.1%
1.6948 × 10-51
< 0.1%
1.6949 × 10-51
< 0.1%
1.7434 × 10-52
< 0.1%
1.7785 × 10-51
< 0.1%
ValueCountFrequency (%)
0.99958 3
< 0.1%
0.99954 1
 
< 0.1%
0.99945 3
< 0.1%
0.99944 4
< 0.1%
0.99943 1
 
< 0.1%
0.99942 2
 
< 0.1%
0.99939 6
0.1%
0.99938 5
< 0.1%
0.99937 2
 
< 0.1%
0.99936 1
 
< 0.1%
Distinct640
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84000257
Minimum0.00041998
Maximum0.99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2025-09-10T16:16:07.145518image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.00041998
5-th percentile0.00302546
Q10.99966
median0.99982
Q30.9999
95-th percentile0.99996
Maximum0.99999
Range0.99957002
Interquartile range (IQR)0.00024

Descriptive statistics

Standard deviation0.36530104
Coefficient of variation (CV)0.43488086
Kurtosis1.4175352
Mean0.84000257
Median Absolute Deviation (MAD)0.00011
Skewness-1.8485384
Sum8506.706
Variance0.13344485
MonotonicityNot monotonic
2025-09-10T16:16:07.239876image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.99989 631
 
6.2%
0.99994 551
 
5.4%
0.99981 478
 
4.7%
0.9999 452
 
4.5%
0.99993 394
 
3.9%
0.99988 391
 
3.9%
0.99982 373
 
3.7%
0.9998 344
 
3.4%
0.99991 341
 
3.4%
0.99969 322
 
3.2%
Other values (630) 5850
57.8%
ValueCountFrequency (%)
0.00041998 2
< 0.1%
0.00042446 1
 
< 0.1%
0.00046237 1
 
< 0.1%
0.00055285 3
< 0.1%
0.00055875 2
< 0.1%
0.0005616 1
 
< 0.1%
0.00056181 1
 
< 0.1%
0.00056902 1
 
< 0.1%
0.00057979 1
 
< 0.1%
0.00058361 1
 
< 0.1%
ValueCountFrequency (%)
0.99999 6
 
0.1%
0.99998 98
 
1.0%
0.99997 260
2.6%
0.99996 183
 
1.8%
0.99995 265
2.6%
0.99994 551
5.4%
0.99993 394
3.9%
0.99992 221
2.2%
0.99991 341
3.4%
0.9999 452
4.5%

Interactions

2025-09-10T16:16:01.954894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:44.052941image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:45.107826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:46.143819image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:47.311240image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:48.323558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:49.559506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:50.697780image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:51.876778image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:52.929217image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:53.997532image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:55.272218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:56.313065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:57.415682image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:58.470011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:59.799048image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:00.809248image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:02.015106image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:44.101547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:45.155968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:46.186473image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:47.387244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:48.375190image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:49.639093image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:50.764309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:51.932936image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:52.991634image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:54.058269image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:55.320391image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:56.373635image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:57.488216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:58.537100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:59.869843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:00.869789image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:02.075726image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:44.156280image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:45.220630image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:46.247275image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:47.440830image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:48.428683image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:49.701458image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:50.819771image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:51.987461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:53.089007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:54.112859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:55.371208image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:56.440210image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:57.543698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:58.600889image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:59.923385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:00.924261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:02.140460image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:44.203735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:45.262113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:46.329889image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:47.501924image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:48.480853image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:49.786109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:50.874263image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:52.044043image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:53.146402image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:54.167532image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:55.435845image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:56.495421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:57.614001image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:58.674802image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:59.978868image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:00.985057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:02.200578image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:44.259159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:45.325592image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:46.392103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:47.549930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:48.532578image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:49.841296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:50.924966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:52.098711image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:53.203602image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:54.219615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:55.488470image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:56.549347image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:57.670170image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:58.731784image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:00.039486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:01.031544image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:02.251903image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:44.313593image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:45.384219image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:46.465622image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:47.598320image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:48.605280image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:49.905890image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:50.977758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:52.171844image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:53.258207image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:54.267978image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:55.541048image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:56.609608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:57.727896image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:58.797818image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:00.094043image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:01.100824image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:02.325730image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:44.372493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:45.470461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:46.531873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:47.650894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:48.687019image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:49.967749image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:51.028249image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:52.238110image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:53.324492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:54.324934image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:55.599170image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:56.655883image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:57.783012image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:58.863861image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:00.154698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:01.197928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:02.401092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:44.422642image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:45.547834image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:46.590166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:47.704017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:48.754730image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:50.015570image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:51.076875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:52.315872image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:53.389826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:54.391537image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:55.655651image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:56.712592image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:57.837605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:58.939642image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:00.215694image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-09-10T16:15:59.078316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:00.336931image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:01.402989image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:02.603032image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:44.590199image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:45.750068image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:46.856816image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:47.895213image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:49.062711image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:50.185778image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:51.270536image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:52.500847image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:53.558954image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:54.597549image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:55.829508image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:56.925558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:58.001217image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:59.148046image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:00.400578image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:01.469786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:02.673198image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:44.653090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:45.810479image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:46.905442image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:47.963236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:49.119403image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-09-10T16:15:53.925315image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:55.209722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:56.232782image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:57.342343image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:58.397315image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:15:59.718450image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:00.754148image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-09-10T16:16:01.882148image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2025-09-10T16:16:07.329097image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Attrition_FlagAvg_Open_To_BuyAvg_Utilization_RatioCLIENTNUMCard_CategoryContacts_Count_12_monCredit_LimitCustomer_AgeDependent_countEducation_LevelGenderIncome_CategoryMarital_StatusMonths_Inactive_12_monMonths_on_bookNaive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2Total_Amt_Chng_Q4_Q1Total_Ct_Chng_Q4_Q1Total_Relationship_CountTotal_Revolving_BalTotal_Trans_AmtTotal_Trans_Ct
Attrition_Flag1.0000.0190.2410.0480.0000.2390.0320.0240.0210.0250.0360.0280.0170.1960.0191.0001.0000.1840.3140.1660.4020.3250.458
Avg_Open_To_Buy0.0191.000-0.6860.0110.3370.0330.931-0.0020.0540.0000.4400.2780.028-0.0160.0080.023-0.0230.007-0.040-0.071-0.1540.0220.022
Avg_Utilization_Ratio0.241-0.6861.0000.0070.149-0.059-0.4170.011-0.0350.0000.2790.1650.027-0.027-0.004-0.1520.1520.0330.0940.0650.7090.0190.040
CLIENTNUM0.0480.0110.0071.0000.0000.0110.0140.017-0.0040.0150.0140.0030.006-0.0080.111-0.0300.0300.0240.0160.0140.003-0.0020.006
Card_Category0.0000.3370.1490.0001.0000.0100.3350.0210.0180.0000.0840.0530.0280.0000.0130.0000.0000.0240.0000.0670.0190.1540.109
Contacts_Count_12_mon0.2390.033-0.0590.0110.0101.0000.023-0.014-0.0410.0000.0590.0150.0070.030-0.0080.644-0.646-0.021-0.0930.061-0.045-0.167-0.168
Credit_Limit0.0320.931-0.4170.0140.3350.0231.0000.0020.0510.0000.4390.2780.026-0.0280.007-0.0230.0230.021-0.011-0.0590.1310.0280.034
Customer_Age0.024-0.0020.0110.0170.021-0.0140.0021.000-0.1440.0140.0000.0840.0820.0440.7690.001-0.002-0.071-0.040-0.0140.014-0.039-0.054
Dependent_count0.0210.054-0.035-0.0040.018-0.0410.051-0.1441.0000.0010.0000.0430.037-0.009-0.1150.045-0.043-0.0260.009-0.036-0.0040.0580.053
Education_Level0.0250.0000.0000.0150.0000.0000.0000.0140.0011.0000.0110.0170.0110.0000.0020.0250.0250.0000.0000.0000.0070.0120.004
Gender0.0360.4400.2790.0140.0840.0590.4390.0000.0000.0111.0000.8390.0090.0190.0110.0360.0360.0440.0500.0000.0330.2470.163
Income_Category0.0280.2780.1650.0030.0530.0150.2780.0840.0430.0170.8391.0000.0080.0170.0460.0280.0280.0150.0230.0070.0220.0930.056
Marital_Status0.0170.0280.0270.0060.0280.0070.0260.0820.0370.0110.0090.0081.0000.0070.0430.0170.0170.0530.0300.0220.0120.1040.099
Months_Inactive_12_mon0.196-0.016-0.027-0.0080.0000.030-0.0280.044-0.0090.0000.0190.0170.0071.0000.0570.558-0.557-0.019-0.047-0.007-0.043-0.032-0.051
Months_on_book0.0190.008-0.0040.1110.013-0.0080.0070.769-0.1150.0020.0110.0460.0430.0571.0000.012-0.013-0.054-0.034-0.0140.006-0.029-0.039
Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_11.0000.023-0.152-0.0300.0000.644-0.0230.0010.0450.0250.0360.0280.0170.5580.0121.000-1.000-0.064-0.213-0.029-0.153-0.210-0.290
Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_21.000-0.0230.1520.0300.000-0.6460.023-0.002-0.0430.0250.0360.0280.017-0.557-0.013-1.0001.0000.0640.2140.0290.1520.2110.290
Total_Amt_Chng_Q4_Q10.1840.0070.0330.0240.024-0.0210.021-0.071-0.0260.0000.0440.0150.053-0.019-0.054-0.0640.0641.0000.3020.0260.0360.1350.085
Total_Ct_Chng_Q4_Q10.314-0.0400.0940.0160.000-0.093-0.011-0.0400.0090.0000.0500.0230.030-0.047-0.034-0.2130.2140.3021.0000.0240.0780.2230.233
Total_Relationship_Count0.166-0.0710.0650.0140.0670.061-0.059-0.014-0.0360.0000.0000.0070.022-0.007-0.014-0.0290.0290.0260.0241.0000.012-0.279-0.227
Total_Revolving_Bal0.402-0.1540.7090.0030.019-0.0450.1310.014-0.0040.0070.0330.0220.012-0.0430.006-0.1530.1520.0360.0780.0121.0000.0180.040
Total_Trans_Amt0.3250.0220.019-0.0020.154-0.1670.028-0.0390.0580.0120.2470.0930.104-0.032-0.029-0.2100.2110.1350.223-0.2790.0181.0000.880
Total_Trans_Ct0.4580.0220.0400.0060.109-0.1680.034-0.0540.0530.0040.1630.0560.099-0.051-0.039-0.2900.2900.0850.233-0.2270.0400.8801.000

Missing values

2025-09-10T16:16:03.118144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-10T16:16:03.309816image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CLIENTNUMAttrition_FlagCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioNaive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2
0768805383Existing Customer45M3High SchoolMarried$60K - $80KBlue3951312691.077711914.01.3351144421.6250.0610.0000930.99991
1818770008Existing Customer49F5GraduateSingleLess than $40KBlue446128256.08647392.01.5411291333.7140.1050.0000570.99994
2713982108Existing Customer51M3GraduateMarried$80K - $120KBlue364103418.003418.02.5941887202.3330.0000.0000210.99998
3769911858Existing Customer40F4High SchoolUnknownLess than $40KBlue343413313.02517796.01.4051171202.3330.7600.0001340.99987
4709106358Existing Customer40M3UneducatedMarried$60K - $80KBlue215104716.004716.02.175816282.5000.0000.0000220.99998
5713061558Existing Customer44M2GraduateMarried$40K - $60KBlue363124010.012472763.01.3761088240.8460.3110.0000550.99994
6810347208Existing Customer51M4UnknownMarried$120K +Gold4661334516.0226432252.01.9751330310.7220.0660.0001230.99988
7818906208Existing Customer32M0High SchoolUnknown$60K - $80KSilver2722229081.0139627685.02.2041538360.7140.0480.0000860.99991
8710930508Existing Customer37M3UneducatedSingle$60K - $80KBlue3652022352.0251719835.03.3551350241.1820.1130.0000450.99996
9719661558Existing Customer48M2GraduateSingle$80K - $120KBlue3663311656.016779979.01.5241441320.8820.1440.0003030.99970
CLIENTNUMAttrition_FlagCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioNaive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2
10117712503408Existing Customer57M2GraduateMarried$80K - $120KBlue4063417925.0190916016.00.712174981110.8200.1060.0005160.999480
10118713755458Attrited Customer50M1UnknownUnknown$80K - $120KBlue366349959.09529007.00.82510310631.1000.0960.9981300.001874
10119716893683Attrited Customer55F3UneducatedSingleUnknownBlue4743314657.0251712140.00.1666009530.5140.1720.9969100.003088
10120710841183Existing Customer54M1High SchoolSingle$60K - $80KBlue3452013940.0210911831.00.660155771140.7540.1510.0000380.999960
10121713899383Existing Customer56F1GraduateSingleLess than $40KBlue504143688.06063082.00.570145961200.7910.1640.0001480.999850
10122772366833Existing Customer50M2GraduateSingle$40K - $60KBlue403234003.018512152.00.703154761170.8570.4620.0001910.999810
10123710638233Attrited Customer41M2UnknownDivorced$40K - $60KBlue254234277.021862091.00.8048764690.6830.5110.9952700.004729
10124716506083Attrited Customer44F1High SchoolMarriedLess than $40KBlue365345409.005409.00.81910291600.8180.0000.9978800.002118
10125717406983Attrited Customer30M2GraduateUnknown$40K - $60KBlue364335281.005281.00.5358395620.7220.0000.9967100.003294
10126714337233Attrited Customer43F2GraduateMarriedLess than $40KSilver2562410388.019618427.00.70310294610.6490.1890.9966200.003377